code: def sample(self):
      x = (
          self.x_prev
          + self.theta * (self.mu - self.x_prev) * self.dt
          + self.current_sigma
          * np.sqrt(self.dt)
          * np.random.normal(size=self.size)
      )
      self.x_prev = x
      self.n_steps += 1
      return x
  
file_path: keras-rl/rl/random.py
imports: from __future__ import division
  import numpy
label_desc: Use image node_id 2 for calling the OrnsteinUhlenbeckProcess obj's underlying member method code with example usage: obj.sample() and returns: x